Audio Encoders & Pre-processing For our audio experiments, we use the M5 classifier [1]} as our encoder. We
also train a simple two-layer network – a static, normalized, log-scattering layer that extracts scattering coefficients from audio signals [2]} followed by a log-softmax layer that generates output probabilities to compare encoder capabilities (Section REF ). Both models are trained only on classes 0-9 from the GSC dataset with the rest of the classes as OOD inputs. In all cases, we learn embeddings directly from raw, one second audio clips (resampled to 8kHz) without making use of any spectrogram features like MFCC [3]}. Raw audio data have dimensionality of \(1 \times 8000 = 8000\) , much larger than the vision datasets.
